Skip to main content
Top
Published in: Journal of Medical Systems 3/2019

01-03-2019 | Computed Tomography | Image & Signal Processing

Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm

Authors: R. Manickavasagam, S. Selvan

Published in: Journal of Medical Systems | Issue 3/2019

Login to get access

Abstract

The Lung nodules are very important to indicate the lung cancer, and its early detection enables timely treatment and increases the survival rate of patient. Even though lots of works are done in this area, still improvement in accuracy is required for improving the survival rate of the patient. The proposed method can classify the stages of lung cancer in addition to the detection of lung nodules. There are two parts in the proposed method, the first part is used for classifying normal/abnormal and second part is used for classifying stages of lung cancer. Totally 10 features from the lung region segmented image are considered for detection and classification. The first part of the proposed method classifies the input images with the aid of Naive Bayes classifier as normal or abnormal. The second part of the system classifies the four stages of lung cancer using Neuro Fuzzy classifier with Cuckoo Search algorithm. The results of proposed system show that the rate of accuracy of classification is improved and the results are compared with SVM, Neural Network and Neuro Fuzzy Classifiers.
Literature
1.
go back to reference Brown, M. S., and McNitt-Gray, M. F., Patient-specific models for lung nodule detection and surveillance in CT images. Proc. IEEE Trans. Med. Imag. 20(12), 2001. Brown, M. S., and McNitt-Gray, M. F., Patient-specific models for lung nodule detection and surveillance in CT images. Proc. IEEE Trans. Med. Imag. 20(12), 2001.
2.
go back to reference Gomathi, M., and Thangaraj, P., Automated CAD for lung nodule detection using CT scans. Proceedings of IEEE Transaction on Data Storage Engineering (DSDE). 150–153, 2010. Gomathi, M., and Thangaraj, P., Automated CAD for lung nodule detection using CT scans. Proceedings of IEEE Transaction on Data Storage Engineering (DSDE). 150–153, 2010.
3.
go back to reference Cosman, P. C., Tseng, C., and Gray, R. M., Tree-structured vector quantization of CT chest scans: Image quality and diagnostic accuracy. Proc. IEEE Trans. Med. Imag. 12(4):727–739, 1993.CrossRef Cosman, P. C., Tseng, C., and Gray, R. M., Tree-structured vector quantization of CT chest scans: Image quality and diagnostic accuracy. Proc. IEEE Trans. Med. Imag. 12(4):727–739, 1993.CrossRef
4.
go back to reference Dewes, P., and Frellesen, C., Comparative evaluation of non-contrast CAIPIRINHA-VIBE 3T-MRI and multidetector CT for detection of pulmonary nodules: In vivo evaluation of diagnostic accuracy and image quality. ELSEVIER J. Eur. J. Radiol. 85(1):193–198, 2016.CrossRef Dewes, P., and Frellesen, C., Comparative evaluation of non-contrast CAIPIRINHA-VIBE 3T-MRI and multidetector CT for detection of pulmonary nodules: In vivo evaluation of diagnostic accuracy and image quality. ELSEVIER J. Eur. J. Radiol. 85(1):193–198, 2016.CrossRef
5.
go back to reference Georg Homann, M. D., and Mustaf, D. F., Improved detection of bone metastases from lung Cancer in the thoracic cage using 5- and 1-mm axial images versus a new CT software generating rib unfolding images. Elsevier J. Acad. Radiol. 22(4):505–512, 2015.CrossRef Georg Homann, M. D., and Mustaf, D. F., Improved detection of bone metastases from lung Cancer in the thoracic cage using 5- and 1-mm axial images versus a new CT software generating rib unfolding images. Elsevier J. Acad. Radiol. 22(4):505–512, 2015.CrossRef
6.
go back to reference Mulshine, J. L., and Gierada, D. S., Role of the quantitative imaging biomarker Alliance in optimizing CT for the evaluation of lung Cancer screen detected nodules. ELSEVIER J. Am. College. Radiol. 12(4):390–395, 2015.CrossRef Mulshine, J. L., and Gierada, D. S., Role of the quantitative imaging biomarker Alliance in optimizing CT for the evaluation of lung Cancer screen detected nodules. ELSEVIER J. Am. College. Radiol. 12(4):390–395, 2015.CrossRef
7.
go back to reference De Nunzio, G., and Massafra, A., Approaches to juxta-pleural nodule detection in CT images within the MAGIC-5 collaboration. ELSEVIER J. Nucl. Instrum. Methods Phys. Res. 648(1):103–106, 2011.CrossRef De Nunzio, G., and Massafra, A., Approaches to juxta-pleural nodule detection in CT images within the MAGIC-5 collaboration. ELSEVIER J. Nucl. Instrum. Methods Phys. Res. 648(1):103–106, 2011.CrossRef
8.
go back to reference Ye, X., Lin, X., and Dehmeshki, J., Shape-based computer-aided detection of lung nodules in thoracic CT images. Proc. IEEE Trans. Biomed. Eng. 56(7):1810–1820, 2009.CrossRef Ye, X., Lin, X., and Dehmeshki, J., Shape-based computer-aided detection of lung nodules in thoracic CT images. Proc. IEEE Trans. Biomed. Eng. 56(7):1810–1820, 2009.CrossRef
9.
go back to reference Gady Agam, S., Vessel tree reconstruction in thoracic CT scans with application to nodule detection. Proc. IEEE Trans. Med. Imag. 24(4):486–499, 2005.CrossRef Gady Agam, S., Vessel tree reconstruction in thoracic CT scans with application to nodule detection. Proc. IEEE Trans. Med. Imag. 24(4):486–499, 2005.CrossRef
10.
go back to reference Ritchie, A. J., and Sanghera, C., Computer vision tool and technician as first reader of lung Cancer screening CT scans. ELSEVIER J. Thor. Oncol. 11(5):709–717, 2016.CrossRef Ritchie, A. J., and Sanghera, C., Computer vision tool and technician as first reader of lung Cancer screening CT scans. ELSEVIER J. Thor. Oncol. 11(5):709–717, 2016.CrossRef
11.
go back to reference Yasaka, K., and Katsura, M., High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction. ELSEVIER Eur. J. Radiol. 85(3):599–605, 2016.CrossRef Yasaka, K., and Katsura, M., High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction. ELSEVIER Eur. J. Radiol. 85(3):599–605, 2016.CrossRef
12.
go back to reference Dehmeshki, J., and Siddique, M., Automated detection of nodules in the CT lung images using multi-modal genetic algorithm. Proc. IEEE Trans. Image Signal Process. Anal. 1:393–398, 2003. Dehmeshki, J., and Siddique, M., Automated detection of nodules in the CT lung images using multi-modal genetic algorithm. Proc. IEEE Trans. Image Signal Process. Anal. 1:393–398, 2003.
13.
go back to reference Tariq, A., and Usman Akram, M., Lung nodule detection in CT images using neuro fuzzy classifier. Proceedings of IEEE transaction on computational intelligence in medical imaging (CIMI). 49–53, 2013. Tariq, A., and Usman Akram, M., Lung nodule detection in CT images using neuro fuzzy classifier. Proceedings of IEEE transaction on computational intelligence in medical imaging (CIMI). 49–53, 2013.
14.
go back to reference Clifford Samuel, C., and Saravanan, V., Lung nodule diagnosis from Ct images using fuzzy logic. Proc. IEEE Int. Conf. Comput. Intell. Multimed. Appl. 3:159–163, 2007. Clifford Samuel, C., and Saravanan, V., Lung nodule diagnosis from Ct images using fuzzy logic. Proc. IEEE Int. Conf. Comput. Intell. Multimed. Appl. 3:159–163, 2007.
15.
go back to reference Han, H., and Li, L., Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. Proc. IEEE Int. J. Biomed. Health Inform. 19(2):648–659, 2015.CrossRef Han, H., and Li, L., Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. Proc. IEEE Int. J. Biomed. Health Inform. 19(2):648–659, 2015.CrossRef
16.
go back to reference Nagatani, Y., and Takahashi, M., Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. ELSEVIER J. Eur. J. Radiol. 84(7):1401–1412, 2015.CrossRef Nagatani, Y., and Takahashi, M., Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. ELSEVIER J. Eur. J. Radiol. 84(7):1401–1412, 2015.CrossRef
17.
go back to reference Valentea, I. R. S., and Cortezb, P. C., Review automatic 3D pulmonary nodule detection in CT images: A survey. ELSEVIER J. Comput. Methods Program Biomed. 124:91–107, 2016.CrossRef Valentea, I. R. S., and Cortezb, P. C., Review automatic 3D pulmonary nodule detection in CT images: A survey. ELSEVIER J. Comput. Methods Program Biomed. 124:91–107, 2016.CrossRef
18.
go back to reference Suia, X., and Meinel, F. G., Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. ELSEVIER J. Eur. J. Radiol. 85(3):564–570, 2016.CrossRef Suia, X., and Meinel, F. G., Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. ELSEVIER J. Eur. J. Radiol. 85(3):564–570, 2016.CrossRef
19.
go back to reference Zhang, H., Han, H., and Liang, Z., Extracting information from previous full-dose CT scan for knowledge-based Bayesian reconstruction of current low-dose CT images. Proc. IEEE Trans. Med. Imag. 35(3):860–870, 2016.CrossRef Zhang, H., Han, H., and Liang, Z., Extracting information from previous full-dose CT scan for knowledge-based Bayesian reconstruction of current low-dose CT images. Proc. IEEE Trans. Med. Imag. 35(3):860–870, 2016.CrossRef
20.
go back to reference Zhoua, S., and Cheng, Y., Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images. ELSEVIER J. Biomed. Signal Process. Contrl. 13:62–70, 2014.CrossRef Zhoua, S., and Cheng, Y., Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images. ELSEVIER J. Biomed. Signal Process. Contrl. 13:62–70, 2014.CrossRef
Metadata
Title
Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm
Authors
R. Manickavasagam
S. Selvan
Publication date
01-03-2019
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 3/2019
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-019-1177-9

Other articles of this Issue 3/2019

Journal of Medical Systems 3/2019 Go to the issue